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Record W2897383949 · doi:10.2351/1.5060551

Stochastic heating in laser materials processing

2005· article· en· W2897383949 on OpenAlexaff
W. W. Duley

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials processingLaserComputer scienceMaterials scienceProcess engineeringOpticsEngineeringPhysics

Abstract

fetched live from OpenAlex

All laser processing applications start with the absorption of photons. This occurs at specific sites (eg. atoms, molecules), with scale sizes of less than 1 nm and must therefore be considered as “nanoprocesses”. In thermal processing of materials heat input is, as a result, spatially modulated on a nanometer scale. Generally, this modulation is unimportant for macro-applications such as cutting and welding but this effect can become significant in the interaction of laser radiation with very small systems. In such systems, each photon can produce a noticeable temperature change and heating becomes stochastic. This aspect of laser heating and its importance in various laser materials processing regimes will be discussed in this paper.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.233
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2005
Admission routes1
Has abstractyes

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